A few months ago I was trying to prepare for a cloud certification exam. I went looking for practice questions - good ones. Not just answer lists, but questions that actually trained the reasoning the exam tests. I found some scattered GitHub repos, a few YouTube playlists, sites with outdated question dumps. Nothing that felt structured. Nothing that explained why an answer was right, not just what it was. So I started building my own study tool. Mock questions, practice sets, AI-generated explanations. The kind of thing I wished existed. Six weeks later that became ArchReady - a certification prep platform for AWS, GCP, and PSM1. It's live now. What it does Practice questions across AWS (CCP, SAA, DVA, SAP), GCP ACE, and PSM1 Explanations for wrong answers - walks through the reasoning, not just the correct option AI-powered explanations coming soon Claude (Anthropic) Confidence tracking - shows which topics you're weak on Free to practice, no signup required. Pro unlocks full history and tracking. The stack Frontend: Next.js 14 (App Router) Backend: FastAPI (Python) AI: Claude (Anthropic) - explanations launching soon Payments: Dodo Hosting: Vercel (web) + Railway (API) Nothing exotic. I kept it boring on purpose - solo founder, 2-5 hrs/week, I can't afford interesting infrastructure problems. What I actually learned Ship before it feels ready. I had a list of 12 features I thought were "required for launch." I launched with 4. Nobody noticed the missing 8. Questions sourced from open-source + AI is good enough to start. Questions come from curated GitHub repos and AI-generated content built around official exam frameworks. That's enough to be useful. Perfection is a later problem. The hardest part isn't building - it's the first 10 users. The product exists. Getting people to try it is the actual work now. Where it is today Live at archready.io . Early stage. Still building. If you're prepping for AWS, GCP, or PSM1 - try it free, no account needed. Honest feedba

TL;DR If Postman’s 2026 Q1 free plan change blocked access to shared collections, your data may still be recoverable. Start with your Postman desktop cache, then check exports, admins, the Postman API, and logs. Once you recover the JSON files, import them into Apidog so your team has a safer workflow going forward. Try Apidog today Introduction After Postman’s 2026 Q1 free tier update, many developers found that shared workspaces were no longer accessible on the free plan. Collections that lived in team workspaces, instead of personal workspaces, became locked behind a paid plan. One developer described it on Reddit: “I came in on Monday and my whole team workspace was gone. Three months of organized collections, environments, all of it. Just gone unless we pay.” In most cases, the data is not immediately deleted. Postman stores workspace data server-side, and the issue is usually access restriction rather than deletion. That said, recovery is time-sensitive because local cache, API access, and workspace availability may not last. Use the steps below in order. 1. Check the Postman desktop app cache first Start with the Postman desktop app, not the web app. The desktop app may still have cached copies of recently opened collections and environments. Even if your server-side access is revoked, the local cache can sometimes keep enough data available to export. Steps Open the Postman desktop app. Do not use the web app at app.getpostman.com . Check the left sidebar for your collections. Open the History tab to confirm which endpoints you recently used. If collections are visible, export them immediately. To export a collection: Right-click the collection or open the three-dot menu. Select Export . Choose Collection v2.1 . Save the file locally. Repeat for every visible collection. If the collection appears but export fails, try working offline: Click your avatar in the top-right corner. Select Go Offline . Retry the export. Going offline can prevent the app from refre

Em resumo Variáveis definidas em scripts do Postman podem “sumir” quando você troca a execução manual pelo Collection Runner. Na prática, quase sempre é um problema de escopo: pm.environment.set escreve no ambiente ativo, variáveis de coleção têm outro ciclo de vida, e o runner pode descartar alterações ao final da execução. Experimente o Apidog hoje Neste guia, você vai ver como diagnosticar o problema, escolher o escopo correto e corrigir os casos mais comuns. Também verá como o Apidog lida com variáveis de forma mais explícita na interface. Introdução Você testa uma API manualmente no Postman: Executa a requisição de login. Um script salva o token. As próximas requisições usam {{token}} . Tudo funciona. Depois você clica em Run Collection . O login retorna sucesso, mas a próxima requisição falha com 401 Unauthorized . O token não foi encontrado. Esse comportamento é comum porque o modo manual e o Collection Runner não lidam com o estado das variáveis exatamente da mesma forma. A correção começa entendendo a hierarquia de escopos do Postman. Hierarquia de escopo de variáveis do Postman O Postman resolve variáveis seguindo uma ordem de prioridade. Da maior para a menor: Variáveis locais : existem apenas durante a execução do script atual. Variáveis de dados : vêm de arquivos CSV ou JSON usados em execuções orientadas por dados. Variáveis de coleção : pertencem à coleção e podem ser usadas por requisições dentro dela. Variáveis de ambiente : pertencem ao ambiente selecionado. Variáveis globais : ficam disponíveis para qualquer coleção e ambiente. Quando você usa: {{token}} o Postman procura token nessa ordem e usa o primeiro valor encontrado. Isso significa que o problema nem sempre é “a variável não existe”. Às vezes ela existe, mas em outro escopo, ou um escopo de maior prioridade está sobrescrevendo o valor esperado. Por que as variáveis desaparecem no Collection Runner 1. Valor inicial vs. valor atual Cada variável no Postman pode ter dois valores: Valor inicial

Something I keep noticing across teams and orgs that are actually getting value from AI versus the ones that aren't. The difference rarely comes down to the model or the algorithm. Most organizations are already drowning in data. Logs, metrics, alerts, reports, dashboards, tickets. The information exists. The bottleneck is what happens after the data shows up. How long does it take to interpret what the signal means? Who decides what to prioritize when three things need attention at once? How fast can the right people coordinate a response once a decision is made? That's where AI actually earns its keep. Not by replacing the human in the loop but by compressing the time between something happening and someone doing something useful about it. Signal to understanding to action. That's the chain that matters. Think about it in terms you deal with every day. A vulnerability gets disclosed. The CVE exists, the advisory is public, your scanner picked it up. None of that is the bottleneck. The bottleneck is figuring out which of your services are affected, who owns them, how bad the exposure actually is in your specific context, and getting a patch scheduled before someone exploits it. AI that helps you answer those questions in minutes instead of days is genuinely valuable. AI that and adds another dashboard to look at isn't. This applies across the board. Incident response, infrastructure management, risk assessment, customer systems, operational workflows. The teams getting real value aren't the ones with the fanciest models. They're the ones who figured out where their decision bottlenecks actually are and pointed AI at those specific gaps. The strategic advantage is rarely in the algorithm. It's in organizational responsiveness. How fast can you go from "something happened" to "we're handling it". The AI is just the thing that compresses that timeline. Where's the biggest decision bottleneck in your current workflow?

If there's one Linked List pattern that every developer should master, it's reversing a Linked List . This problem teaches pointer manipulation, which later helps in solving problems like: Reverse Nodes in K Group Palindrome Linked List Reorder List Reverse Between Positions Let's understand the intuition behind it. Problem Statement Given the head of a singly linked list, reverse the list and return the new head. Example Input: 1 -> 2 -> 3 -> 4 -> 5 Output: 5 -> 4 -> 3 -> 2 -> 1 Brute Force Approach Intuition A straightforward approach is to store all node values inside an ArrayList. Then traverse the linked list again and replace node values from the array in reverse order. Although it works, we're using extra space just to remember previous elements. Complexity Time Complexity : O ( N ) Space Complexity : O ( N ) Brute Force Code class Solution { public ListNode reverseList ( ListNode head ) { ArrayList < Integer > values = new ArrayList <>(); ListNode temp = head ; while ( temp != null ) { values . add ( temp . val ); temp = temp . next ; } temp = head ; int index = values . size () - 1 ; while ( temp != null ) { temp . val = values . get ( index --); temp = temp . next ; } return head ; } } Moving Towards the Optimal Approach Instead of storing values separately, can we reverse the actual links between nodes? Since Linked Lists are made of pointers, reversing those pointers directly would save extra space. This leads us to the optimal solution. Optimal Approach - Three Pointer Technique We'll maintain three pointers: prev curr next For every node: Store the next node. Reverse the current node's pointer. Move all pointers one step ahead. Visual Intuition Initially: null <- 1 -> 2 -> 3 -> 4 -> 5 ^ curr After first reversal: null <- 1 2 -> 3 -> 4 -> 5 ^ prev ^ curr Continue the same process until the list ends. Dry Run Input 1 -> 2 -> 3 -> null Initial State prev = null curr = 1 Iteration 1 next = 2 1 -> null prev = 1 curr = 2 Iteration 2 next = 3 2 -> 1 -> null p

A tweet landed on my timeline from Peter Steinberger — OpenClaw founder, now at OpenAI: "Here's...

When developers think about privacy, they often think about legal compliance, consent banners, or policy pages. But privacy starts much earlier than that, at the API layer. Every time your backend asks for a phone number, date of birth, location, or optional profile field, you are making a design decision. If you collect too much data by default, you increase risk, reduce trust, and make your system harder to maintain. The good news is that privacy by design does not have to make your product worse. In many cases, it makes your API cleaner, safer, and easier to reason about. Why collecting less data matters The more data you collect, the more you have to protect. That means more storage, more access control, more breach risk, more compliance burden, and more user distrust if something goes wrong. A developer friendly privacy approach is simple: Only collect what you need to deliver value. Bad pattern: collecting everything up front user_profile = { " name " : " Amina " , " email " : " amina@example.com " , " phone " : " 07000000000 " , " dob " : " 1995-01-01 " , " address " : " 123 Main Street " , " location " : " Lagos " , " gender " : " female " } This kind of structure is common in early stage products. The thinking is usually: let us ask for everything now, just in case we need it later. But just in case is not a good privacy strategy. Better pattern: collect only what is required def build_profile ( name , email , phone = None ): profile = { " name " : name , " email " : email } if phone is not None : profile [ " phone " ] = phone return profile user_profile = build_profile ( " Amina " , " amina@example.com " ) print ( user_profile ) This is small, but the principle matters. Optional data should stay optional unless it is truly needed. Use explicit field validation Instead of accepting a giant payload and filtering it later, validate the exact fields you expect. ALLOWED_FIELDS = { " name " , " email " , " phone " } def sanitize_payload ( payload ): return { k :

Built a small scripted pipeline to benchmark accessibility on 100 German online shops and the numbers were rougher than I expected, so here is the setup in case it is useful for your own CI. Stack: Puppeteer drives a headless Chromium through up to five routes per shop (home through checkout). Then pa11y 9.1.1 runs HTML_CodeSniffer and axe-core 4.10.2 runs on the same loaded DOM. Results get deduped by selector and rule id so the two engines do not double-count. Shops were picked to match German platform share. Shopify was the biggest block at 40 of 100, with Shopware and WooCommerce next. Output: 29,745 hard errors across the sample, with every one of the 100 shops failing WCAG 2.1 AA and homepages averaging 99.8 errors. The recurring offenders were touch targets under 44px on all 100, low contrast on 67, broken heading order on 61 and unnamed links on 58. Two practical notes for anyone scripting this. Checkout was only reachable on 82 of 100 without an account or a real cart, so deep-page coverage is uneven and you should log it per route instead of pretending you scanned everything. And automated detection is about 57% of real issues, so this is a smoke test, not an audit. submitted by /u/Loewenkompass [link] [留言]

Turn any Figma shape into liquid metal in one click Discussion | Link

Hellblade developer Ninja Theory is no longer working on Project Mara, a psychological horror game it announced in 2020.

submitted by /u/BlondieCoder [link] [留言]

Context engineering matters for two reasons: reliability and cost. If your agent's context window is...

I tried to build one before but I think I’m seeing the same results. Any tips on how to effectively build a gpt in chatgpt? submitted by /u/GlobalOpsNotes [link] [留言]

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